Extended measurement method based on tilt measurement technology and GNSS receiver

Abstract

The present invention discloses an extended measurement method and a GNSS receiver based on tilt measurement technology. The extended measurement method includes: collecting GNSS observation data and differential data and IMU sampling data of a sliding window, the sliding window including a measurement start point, a measurement end point and a position to be measured, and the position to be measured is located between the measurement start point and the measurement end point; constructing a nonlinear least squares problem using all the GNSS observation data and differential data and IMU sampling data in the sliding window; and estimating the positioning solution of the position to be measured using the nonlinear least squares problem. The present invention enables users to easily achieve centimeter-level precision measurements in an obscured environment, and can increase the precision retention time to tens of seconds, allowing users to easily achieve high-precision measurements in an obscured environment. The extended measurement technology is a quasi-real-time technology, rather than a post-processing technology. Users can instantly obtain accurate point coordinates in a point library, which has little impact on the user's current workflow and is very convenient.

Description

Technical Field

[0001] The present invention relates to an extension measurement method based on tilt measurement technology and a GNSS receiver. Background Art

[0002] In recent years, inertial measurement unit-based tilt measurement has been increasingly popularized and applied. The GNSS receiver's built-in inertial orientation navigation system (INS) outputs real-time GNSS receiver attitude data, allowing calculation of the mast's tilted azimuth, tilt angle, and tilt direction. Combined with the acquired GNSS receiver antenna phase center coordinates, the coordinates of the ground point at the mast's base can be calculated.

[0003] In locations where satellite signals are severely obstructed or multipath effects are severe, such as under the shade of trees, under eaves, or at the corners of two high walls, GNSS mapping terminals struggle to achieve reliable centimeter-level accuracy through direct measurement. (Direct measurement is the current measurement method for GNSS mapping terminals, which involves waiting for RTK fixes at the measured point and then using vertical or oblique projection to obtain the coordinates of the measured point.) This is because the number of available GNSS RTK satellites is too small, or the available satellites have significant multipath observation errors, making it difficult for RTK to accurately fix or resulting in poor fix solution accuracy.

[0004] If a GNSS surveying terminal is not used, a total station is generally required, which takes a long time to measure points and greatly reduces work efficiency. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defect of poor measurement accuracy in locations with obstructions or severe multipath effects in the prior art, and to provide an extended measurement method and GNSS receiver based on tilt measurement technology that can easily achieve centimeter-level accuracy in target point measurement in an obstructed environment, and can increase the accuracy retention time to tens of seconds, so that users can easily achieve high-precision measurement in an obstructed environment.

[0006] The present invention solves the above technical problems through the following technical solutions:

[0007] An extended measurement method based on tilt measurement technology is characterized in that the extended measurement method includes:

[0008] Collecting GNSS observation data and differential data and IMU sampling data of a sliding window, wherein the sliding window includes a measurement start point, a measurement end point, and a position to be measured, wherein the position to be measured is located between the measurement start point and the measurement end point;

[0009] A nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window;

[0010] The nonlinear least squares problem is used to estimate the positioning solution of the position to be measured.

[0011] Preferably, the extension measurement method includes:

[0012] Based on the quality of the observations obtained around the location to be measured;

[0013] A first position where the observation quality is greater than a preset observation quality is selected as the measurement starting point, and a second position where the observation quality is greater than the preset observation quality is selected as the measurement end point.

[0014] Preferably, obtaining the observation quality around the position to be measured comprises: obtaining the observation quality according to the residual error of each satellite positioning, the satellite geometric precision factor, and the carrier-to-noise ratio of each satellite;

[0015] The distance from the first position to the position to be measured and the distance from the second position to the position to be measured both belong to a preset length range.

[0016] Preferably, the extension measurement method includes:

[0017] A nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window;

[0018] Obtaining parameters to be estimated using the nonlinear least squares problem, wherein the estimated parameters include parameter data of the estimated position to be measured, and the parameter data includes position data, velocity data, and posture data at each sampling moment in the sliding window;

[0019] Obtain the positioning solution of the position to be measured based on the parameters to be estimated.

[0020] Preferably, the nonlinear least squares problem is solved using a sparse matrix to obtain the parameters to be estimated.

[0021] Preferably, the extension measurement method includes:

[0022] After starting measurement at the measurement starting point, detecting whether a measurement instruction is received, and if so, obtaining the measurement time when the measurement instruction is received;

[0023] The positioning solution of the position to be measured is obtained based on the parameters to be estimated at the measurement time.

[0024] Preferably, the extension measurement method includes:

[0025] For each observation point, the global FDE technology is used to determine whether there is a faulty observation point among the observation points. If so, the measurement data of the faulty observation point is excluded and the steps of constructing the nonlinear least squares problem using all the GNSS observation data and differential data and IMU sampling data in the sliding window are executed again.

[0026] Preferably, the extension measurement method includes:

[0027] For each observation point, the normalized residual of the observation point is obtained using the GNSS observation data and differential data, IMU sampling data and covariance, and the parameters to be estimated;

[0028] Determine whether the normalized residual is greater than a threshold. If so, determine that the observation point with the normalized residual greater than the threshold is a fault observation point.

[0029] The present invention further provides an extended measurement system, which is characterized in that the extended measurement system includes a GNSS receiver and a processing module, and is used to implement the extended measurement method described above.

[0030] The present invention further provides a GNSS receiver, which is characterized in that the GNSS receiver includes a processing unit, and the processing unit is used to implement the extension measurement method as described above.

[0031] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0032] The positive progress effect of the present invention is:

[0033] This invention enables users to easily measure target points with centimeter-level accuracy in obscured environments, maintaining accuracy for tens of seconds, making it easy to achieve high-precision measurements in obscured environments. Furthermore, the extended measurement technology operates in near real-time, rather than post-processing, allowing users to retrieve precise point coordinates from the point library after only a few seconds, minimizing the impact on current workflows and providing significant convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the structure of a GNSS receiver according to embodiment 1 of the present invention.

[0035] Figure 2 This is a flow chart of the extended measurement method according to embodiment 1 of the present invention. DETAILED DESCRIPTION

[0036] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples.

[0037] Example 1

[0038] See also Figure 1 This embodiment provides a GNSS receiver with a tilt measurement function, wherein the receiver includes an inertial measurement unit for implementing the tilt measurement function.

[0039] The GNSS receiver includes a collection module 11 , a processing module 12 and a positioning module 13 .

[0040] The acquisition module is used to acquire GNSS observation data and differential data and IMU sampling data of a sliding window, wherein the sliding window includes a measurement start point, a measurement end point and a position to be measured, and the position to be measured is located between the measurement start point and the measurement end point.

[0041] GNSS observation data and differential data are the position, velocity, and time information collected by Global Navigation Satellite System (GNSS) receivers. This data is typically stored in a specific format, such as RINEX (Receiver Independent Exchange Format) or NMEA (National Marine Electronics Association).

[0042] IMU sampled data refers to data from an inertial navigation system (INS). An INS is a technology that determines an object's position, velocity, and orientation by measuring and integrating data from accelerometers and gyroscopes. IMU sampled data includes accelerometer and gyroscope measurements. In this embodiment, IMU sampled data is used to represent the attitude of the GNSS receiver.

[0043] In the specification and claims of this application, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, product or apparatus comprising a series of method steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0044] The processing module is used to construct a nonlinear least squares problem using all GNSS observation data and differential data and IMU sampling data within the sliding window;

[0045] The positioning module is used to estimate the positioning solution of the position to be measured by using the nonlinear least squares problem.

[0046] The GNSS receiver further includes a scene recognition module 14 and a selection module 15 .

[0047] The scene recognition module is used to obtain observation quality around the position to be measured;

[0048] The selection module is used to select a first position where the observation quality is greater than a preset observation quality as the measurement starting point, and select a second position where the observation quality is greater than the preset observation quality as the measurement end point.

[0049] It should be noted that the terms "first," "second," etc. in the specification and claims of this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It is understood that the terms can be interchanged under appropriate circumstances.

[0050] In this embodiment, the first position and the second position may be the same spatial location or different spatial locations.

[0051] Specifically, the observation quality is obtained around the position to be measured: the observation quality is obtained according to the residual error of each satellite positioning, the satellite geometric precision factor, and the carrier-to-noise ratio of each satellite.

[0052] The distance from the first position to the position to be measured and the distance from the second position to the position to be measured both belong to a preset length range.

[0053] The scene recognition module is implemented by the following equation:

[0054] Q=f(r,d,n)

[0055] Where Q is the observation quality, which is used to distinguish the severity of scene occlusion, r is the residual error of each satellite's positioning, d is the satellite's geometric dilution of precision (GDOP), n is the carrier-to-noise ratio of each satellite, and f is a function that characterizes the mapping relationship from several parameters to observation quality.

[0056] A certain observation quality threshold is set for Q. When the observation quality reaches a certain value, the device will consider the user to be in a good observation environment and select the starting and ending points of the optimization window accordingly.

[0057] The sliding window should be selected carefully. A larger sliding window will increase the system's computational burden and reduce real-time performance, while a smaller sliding window will reduce system performance. Therefore, the distance from the first position to the position to be measured and the distance from the second position to the position to be measured both fall within a preset length range.

[0058] Specifically, the processing module is used to construct a nonlinear least squares problem using all GNSS observation data and differential data and IMU sampling data within the sliding window;

[0059] The processing module is further configured to obtain parameters to be estimated using the nonlinear least squares problem, wherein the estimated parameters include parameter data of the estimated position to be measured, and the parameter data includes position data, velocity data, and posture data at each sampling moment within the sliding window;

[0060] The positioning module is used to obtain a positioning solution for the position to be measured based on the parameters to be estimated.

[0061] Using all GNSS and INS measurements (IMU sampling data) within the above sliding window, the following nonlinear least squares problem is constructed:

[0062]

[0063] Among them, χ is the parameter to be estimated, including the position, velocity, attitude and other parameters at each moment in the window, z r and z I are the measurement values ​​of GNSS and INS, h r and h I are the nonlinear measurement equations of GNSS and INS respectively.

[0064] The nonlinear least squares problem is solved using a sparse matrix to obtain the parameters to be estimated.

[0065] For large-scale optimization problems, this embodiment adopts sparse matrix theory to solve them, which greatly shortens the solution time while meeting the accuracy requirements to meet quasi-real-time requirements.

[0066] Furthermore, the processing module is configured to detect whether a measurement instruction is received after starting measurement at the measurement starting point, and if so, obtain the measurement time at which the measurement instruction is received;

[0067] The positioning module is used to obtain a positioning solution for the position to be measured based on the parameters to be estimated at the measurement moment.

[0068] Furthermore, the GNSS module further includes a troubleshooting module for:

[0069] For each observation point, the global FDE technology is used to determine whether there is a faulty observation point among the observation points. If so, the measurement data of the faulty observation point is excluded and the step of constructing a nonlinear least squares problem using all the GNSS observation data and differential data and IMU sampling data in the sliding window is executed again, that is, the processing module is notified to execute the nonlinear least squares problem constructed using all the GNSS observation data and differential data in the sliding window (excluding the GNSS observation data and differential data of the faulty observation point) and IMU sampling data.

[0070] Specifically, the troubleshooting module is used to:

[0071] For each observation point, the normalized residual of the observation point is obtained using the GNSS observation data and differential data, IMU sampling data and covariance, and the parameters to be estimated;

[0072] The covariance and parameters to be estimated refer to the covariance and parameters to be estimated of GNSS observation data and differential data, and IMU sampling data.

[0073] Determine whether the normalized residual is greater than a threshold. If so, determine that the observation point with the normalized residual greater than the threshold is a fault observation point.

[0074] To further improve reliability, the GNSS receiver of this embodiment uses global FDE technology to simultaneously detect and troubleshoot faults for all observations within the window. The fault detection criteria are as follows:

[0075]

[0076] Among them, the subscript i represents the observation value of a certain observation point, R is the variance of the observation value, r thr is the threshold for normalizing the residual.

[0077] After the faults are eliminated, the algorithm will optimize the sliding window again. The above process of eliminating faults and optimizing will be repeated until no new faults appear.

[0078] The "extended measurement" technology proposed in this embodiment is achieved through quasi-real-time GNSS / INS integrated navigation post-processing technology.

[0079] This technology requires the user to move the device from an open sky environment to a target location in a poor signal environment, click to start the measurement, pause at the target location for a few seconds (1-5 seconds), and then move the device back to an open sky environment. After completing these measurements, the device automatically performs near-real-time post-processing on the entire data segment to calculate the high-precision coordinates of the measured point.

[0080] In traditional direct measurement, the measurement accuracy of a point depends on the accuracy of the RTK fixed solution when the device is at that point. In "extended measurement", the measurement of a point also adds forward and backward motion constraints based on inertial navigation, so that the coordinate accuracy obtained in open sky areas can be "extended" to poor signal scenarios, thereby improving measurement accuracy in poor signal scenarios.

[0081] If traditional measurement methods are used, the process of switching the user's motion scenes (open, blocked, open) will result in different measurement accuracies, namely centimeters, decimeters, and centimeters. However, by extending the measurement technology, the above can be optimized to continuous centimeter-level accuracy.

[0082] Since the accuracy of the GNSS / INS integrated navigation post-processing algorithm in an obscured environment can be improved by 5 to 10 times compared to the real-time processing algorithm, after using the extended measurement technology, the user can stay in the obscured environment for tens of seconds, which is much longer than the few seconds described in the background technology. This has made the measurement operation (being in an obscured environment for an extremely short time) from impossible to easily achievable.

[0083] Compared with the traditional GNSS / INS real-time estimation algorithm based on extended Kalman filter (EKF), the design of the proposed quasi-real-time post-processing algorithm is more complex.

[0084] Simply put, the technology can be divided into three parts: scene recognition, global optimization, and fault detection and elimination (FDE).

[0085] Among them, scene recognition technology is responsible for distinguishing the above-mentioned occlusion and open scenes, so as to determine the appropriate starting and ending points for the next step of global optimization. The global optimization algorithm performs unified nonlinear optimization on the GNSS and IMU sampling data within the above-mentioned selected window to obtain a high-precision positioning solution. Based on the global optimization, FDE is responsible for identifying large error points and RTK incorrectly fixed points and eliminating them to ensure system reliability.

[0086] Furthermore, by taking advantage of the fact that open areas have high accuracy and obstructed areas have low accuracy, when moving from a high-accuracy area to a low-accuracy area, the accuracy will drop rapidly in about 1 to 3 seconds. Utilizing this characteristic, the GNSS receiver of this embodiment can automatically adjust the size of the sliding window. When the sliding window size is large, the data collected by the sliding window for a preset time (within 1 second) after the measurement starting point is deleted, thereby improving the calculation speed.

[0087] This embodiment enables users to easily measure target points with centimeter-level accuracy in obscured environments, maintaining accuracy for tens of seconds. This makes high-precision measurement possible in obscured environments. Furthermore, the extended measurement technology operates in near real-time, rather than post-processing, allowing users to retrieve precise point coordinates from the point library in just a few seconds, minimizing the impact on current workflows and providing significant convenience.

[0088] See also Figure 2 , using the above-mentioned GNSS receiver based on the tilt measurement technology, this embodiment further provides an extended measurement method, including:

[0089] Step 100: Obtain observation quality around the position to be measured;

[0090] Step 101: Select a first position where the observation quality is greater than a preset observation quality as the measurement starting point, and select a second position where the observation quality is greater than the preset observation quality as the measurement end point.

[0091] The obtaining of the observation quality around the position to be measured is as follows: obtaining the observation quality according to the residual error of each satellite positioning, the satellite geometric precision factor, and the carrier-to-noise ratio of each satellite;

[0092] The distance from the first position to the position to be measured and the distance from the second position to the position to be measured both belong to a preset length range.

[0093] Specifically, the scene recognition module is implemented by the following equation:

[0094] Q=f(r,d,n)

[0095] Where Q is the observation quality, which is used to distinguish the severity of scene occlusion, r is the residual error of each satellite's positioning, d is the satellite's geometric dilution of precision (GDOP), n is the carrier-to-noise ratio of each satellite, and f is a function that characterizes the mapping relationship from several parameters to observation quality.

[0096] A certain observation quality threshold is set for Q. When the observation quality reaches a certain value, the device will consider the user to be in a good observation environment and select the starting and ending points of the optimization window accordingly.

[0097] Step 102: Collect GNSS observation data and differential data and IMU sampling data of a sliding window, wherein the sliding window includes a measurement start point, a measurement end point, and a position to be measured, and the position to be measured is located between the measurement start point and the measurement end point.

[0098] Step 103: A nonlinear least squares problem is constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window.

[0099] Step 104: Estimate the positioning solution of the position to be measured using the nonlinear least squares problem.

[0100] Wherein, step 103 specifically includes:

[0101] Step 1031: A nonlinear least squares problem is constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window.

[0102] Step 1032: Obtain parameters to be estimated using the nonlinear least squares problem, where the estimated parameters include parameter data of the estimated position to be measured, and the parameter data includes position data, velocity data, and posture data at each sampling moment within the sliding window;

[0103] Specifically, step 104 is: obtaining a positioning solution for the position to be measured according to the parameters to be estimated.

[0104] In step 1032, the nonlinear least squares problem is solved using a sparse matrix to obtain the parameters to be estimated.

[0105] Step 105: For each observation point, obtain the normalized residual of the observation point using the GNSS observation data and differential data, IMU sampling data and covariance, and the parameters to be estimated.

[0106] Step 106 : Determine whether the normalized residual is greater than a threshold. If so, execute step 107 ; otherwise, execute step 109 .

[0107] Step 107: Determine an observation point whose normalized residual is greater than a threshold as a fault observation point.

[0108] For each observation point, the global FDE technology is used to determine whether there is a fault observation point among the observation points.

[0109] Step 108 : Eliminate the measurement data of the fault observation point, and then execute step 103 .

[0110] Step 109: Output the positioning solution.

[0111] The extended measurement method of this embodiment enables users to easily achieve centimeter-level precision target point measurement in an obscured environment, and can increase the accuracy retention time to tens of seconds, allowing users to easily achieve high-precision measurement in an obscured environment.

[0112] In addition, the extended measurement technology is a quasi-real-time technology rather than a post-processing technology. Users only need to delay for a few seconds to obtain the precise point coordinates in the point library, which has little impact on the user's current workflow and is very convenient.

[0113] Example 2

[0114] This embodiment is basically the same as the first embodiment, except that:

[0115] This embodiment provides an extended measurement system based on tilt measurement technology. The extended measurement system includes a GNSS receiver and a processing device. The processing device can be a tablet computer, a server, or a portable mobile processing terminal.

[0116] The GNSS receiver is used to collect GNSS observation data and differential data of a sliding window and IMU sampling data, wherein the sliding window includes a measurement start point, a measurement end point and a position to be measured, and the position to be measured is located between the measurement start point and the measurement end point.

[0117] After acquiring the GNSS observation data, differential data and IMU sampling data, the GNSS receiver transmits the GNSS observation data, differential data and IMU sampling data to the processing device via wired or wireless means.

[0118] The processing device is used for:

[0119] Perform nonlinear optimization processing on the collected GNSS observation data, differential data and IMU sampling data;

[0120] The nonlinear optimization processing result is used to estimate the positioning solution of the position to be measured.

[0121] The GNSS receiver or processing device outputs the positioning solution.

[0122] The GNSS receiver is used to:

[0123] Based on the quality of the observations obtained around the location to be measured;

[0124] A first position where the observation quality is greater than a preset observation quality is selected as the measurement starting point, and a second position where the observation quality is greater than the preset observation quality is selected as the measurement end point.

[0125] The GNSS receiver is used to:

[0126] Obtaining the observation quality based on the residual error of each satellite positioning, the satellite geometric precision factor, and the carrier-to-noise ratio of each satellite;

[0127] The distance from the first position to the position to be measured and the distance from the second position to the position to be measured both belong to a preset length range.

[0128] The processing device is used for:

[0129] A nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window;

[0130] Obtaining parameters to be estimated using the nonlinear least squares problem, wherein the estimated parameters include parameter data of the estimated position to be measured, and the parameter data includes position data, velocity data, and posture data at each sampling moment in the sliding window;

[0131] Obtain the positioning solution of the position to be measured based on the parameters to be estimated.

[0132] The processing device is used to solve the nonlinear least squares problem using a sparse matrix to obtain the parameters to be estimated.

[0133] GNSS receivers are used for:

[0134] After starting measurement at the measurement starting point, detecting whether a measurement instruction is received, and if so, obtaining the measurement time when the measurement instruction is received;

[0135] The measurement time is sent to the processing device.

[0136] The processing device obtains a positioning solution for the position to be measured based on the parameters to be estimated at the measurement moment.

[0137] The processing device is used for:

[0138] For each observation point, the global FDE technology is used to determine whether there is a faulty observation point among the observation points. If so, the measurement data of the faulty observation point is excluded and the steps of constructing the nonlinear least squares problem using all the GNSS observation data and differential data and IMU sampling data in the sliding window are executed again.

[0139] Specifically, for each observation point, the normalized residual of the observation point is obtained using the GNSS observation data and differential data, IMU sampling data and covariance, and the parameters to be estimated;

[0140] Determine whether the normalized residual is greater than a threshold. If so, determine that the observation point with the normalized residual greater than the threshold is a fault observation point.

[0141] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. An extended measurement method based on tilt measurement technology, characterized in that: The extension measurement method comprises: Collecting GNSS observation data and differential data and IMU sampling data of a sliding window, wherein the sliding window includes a measurement start point, a measurement end point, and a position to be measured, wherein the position to be measured is located between the measurement start point and the measurement end point; A nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window; estimating a positioning solution of the position to be measured using the nonlinear least squares problem; The extension measurement method comprises: Based on the quality of the observations obtained around the location to be measured; Selecting a first position where the observation quality is greater than a preset observation quality as the measurement starting point, and selecting a second position where the observation quality is greater than the preset observation quality as the measurement end point; The observation quality is obtained around the position to be measured according to: obtaining the observation quality according to the residual error of each satellite positioning, the satellite geometric precision factor and the carrier-to-noise ratio of each satellite; The distance from the first position to the position to be measured and the distance from the second position to the position to be measured both belong to a preset length interval; The extension measurement method comprises: For each observation point, the global FDE technology is used to determine whether there is a faulty observation point among the observation points. If so, the measurement data of the faulty observation point is excluded and the nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data in the sliding window is executed again; The extension measurement method comprises: For each observation point, the normalized residual of the observation point is obtained using the GNSS observation data and differential data, IMU sampling data and covariance, and the parameters to be estimated; Determine whether the normalized residual is greater than a threshold. If so, determine that the observation point with the normalized residual greater than the threshold is a fault observation point.

2. The extension measurement method according to claim 1, wherein: The extension measurement method comprises: A nonlinear least squares problem constructed using all GNSS observation data and differential data and IMU sampling data within the sliding window; Obtaining parameters to be estimated using the nonlinear least squares problem, wherein the parameters to be estimated include parameter data of the estimated position to be measured, and the parameter data includes position data, velocity data, and posture data at each sampling moment in the sliding window; Obtain the positioning solution of the position to be measured based on the parameters to be estimated.

3. The extension measurement method according to claim 2, wherein: The nonlinear least squares problem is solved using a sparse matrix to obtain the parameters to be estimated.

4. The extension measurement method according to claim 3, wherein: The extension measurement method comprises: After starting measurement at the measurement starting point, detecting whether a measurement instruction is received, and if so, obtaining the measurement time when the measurement instruction is received; The positioning solution of the position to be measured is obtained based on the parameters to be estimated at the measurement time.

5. An extension measurement system, characterized in that: The extended measurement system includes a GNSS receiver and a processing module, and is used to implement the extended measurement method according to any one of claims 1 to 4.

6. A GNSS receiver, characterized in that: The GNSS receiver comprises a processing unit, and the processing unit is configured to implement the extension measurement method according to any one of claims 1 to 4.

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